{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNTK 106:  Part B - Time series prediction with LSTM (IOT Data)\n",
    "\n",
    "In [part A of this tutorial](CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb) we developed a simple LSTM network to predict future values in a time series. In part B we want to use the model on some real world internet-of-things ([IOT](https://en.wikipedia.org/wiki/Internet_of_things)) data. As an example we want to predict the daily output of a solar panel base on the initial readings of the day. \n",
    "\n",
    "[Solar power forecasting](https://en.wikipedia.org/wiki/Solar_power_forecasting) is a challenging and important problem. The solar energy generation forecasting problem is closely linked to the problem of weather variables forecasting. Indeed, this problem is usually split into two parts, on one hand focusing on the forecasting of solar PV or any other meteorological variable and on the other hand estimating the amount of energy that a concrete power plant will produce with the estimated meteorological resource. In general, the way to deal with this difficult problem is usually related to the spatial and temporal scales we are interested in. This tutorial focusses on a simplified forecasting model using previously generated data from solar panel to predict the future. \n",
    "\n",
    "**Goal**\n",
    "\n",
    "Using historic daily production of a solar panel, we want to predict the total power production of the solar panel array for a day. We will be using the LSTM based time series prediction model developed in part A to predict the daily output of a solar panel based on the initial readings of the day. \n",
    "\n",
    "![](https://www.cntk.ai/jup/rooftop-solar-power.jpg)\n",
    "\n",
    "We train the model with historical data of the solar panel. In our example we want to predict the total power production of the solar panel array for the day starting with the initial readings of the day. We start predicting after the first 2 readings and adjust the prediction with each new reading.\n",
    "\n",
    "In this tutorial, we will use the LSTM model introduced in the CNTK 106A. This tutorial has the following sub-sections:\n",
    "- Setup\n",
    "- Data generation\n",
    "- LSTM network modeling\n",
    "- Model training and evaluation\n",
    "\n",
    "For more details on how LSTMs work, see [this excellent post](http://colah.github.io/posts/2015-08-Understanding-LSTMs)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We need a few imports and constants throughout the tutorial that we define here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import random\n",
    "import time\n",
    "\n",
    "import cntk as C\n",
    "\n",
    "try:\n",
    "    from urllib.request import urlretrieve\n",
    "except ImportError:\n",
    "    from urllib import urlretrieve\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import cntk.tests.test_utils\n",
    "cntk.tests.test_utils.set_device_from_pytest_env() # (only needed for our build system)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to make things reproduceable, seed random\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two run modes:\n",
    "- *Fast mode*: `isFast` is set to `True`. This is the default mode for the notebooks, which means we train for fewer iterations or train / test on limited data. This ensures functional correctness of the notebook though the models produced are far from what a completed training would produce.\n",
    "\n",
    "- *Slow mode*: We recommend the user to set this flag to `False` once the user has gained familiarity with the notebook content and wants to gain insight from running the notebooks for a longer period with different parameters for training. \n",
    "\n",
    "For *Fast mode* we train the model for 100 epochs and results have low accuracy but is good enough for development. The model yields good accuracy after 1000-2000 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "isFast = True\n",
    "\n",
    "# we need around 2000 epochs to see good accuracy. For testing 100 epochs will do.\n",
    "EPOCHS = 100 if isFast else 2000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data generation\n",
    "\n",
    "Our solar panel, emits two measures at every 30 min interval:\n",
    "- `solar.current` is the current production in Watt\n",
    "- `solar.total` is the total produced for the day so far in Watt/hour\n",
    "\n",
    "Our prediction approach involves starting with the first 2 initial readings of the day. Based on these readings we start predicting and adjust the prediction with each new reading. The training data we are going to use comes as a csv file and has the following format:\n",
    "\n",
    ">```\n",
    "time,solar.current,solar.total\n",
    "7am,6.3,1.7\n",
    "7:30am,44.3,11.4\n",
    "...\n",
    ">```\n",
    "\n",
    "The training dataset we use contains data captured for 3 years and can be found [here](https://guschmueds.blob.core.windows.net/datasets/solar.csv). \n",
    "The dataset is not pre-processed: it is raw data and contains smaller gaps and errors (like a panel failed to report)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing\n",
    "Most of the code in this example is related to data preparation. Thankfully the pandas library make this easy.\n",
    "\n",
    "`generate_solar_data()` function performs the following tasks:\n",
    "- read raw data into a pandas dataframe, \n",
    "- normalize the data, \n",
    "- groups by day and \n",
    "- append the columns \"solar.current.max\" and \"solar.total.max\", and\n",
    "- generates the sequences for each day.\n",
    "\n",
    "*Sequence generation*: All sequences are concatenated into a single list of sequences. There is *no more notion of timestamp* in our train input and **only** the sequences matter.\n",
    "\n",
    "**Note** if we have less than 8 datapoints for a day we skip over the day assuming something is missing in the raw data. If we get more than 14 data points in a day we truncate the readings.\n",
    "\n",
    "### Training / Testing / Validation data preparation\n",
    "We start by reading the csv file for use with CNTK.  The raw data is sorted by time and we should randomize it before splitting into training, validation and test datasets but this would make it impractical to visualize results in the tutorial. Hence, we split the dataset in the following manner: pick in sequence, 8 values for training, 1 for validation and 1 for test until there is no more data. This will spread training, validation and test datasets across the full timeline while preserving time order.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_solar_data(input_url, time_steps, normalize=1, val_size=0.1, test_size=0.1):\n",
    "    \"\"\"\n",
    "    generate sequences to feed to rnn based on data frame with solar panel data\n",
    "    the csv has the format: time ,solar.current, solar.total\n",
    "     (solar.current is the current output in Watt, solar.total is the total production\n",
    "      for the day so far in Watt hours)\n",
    "    \"\"\"\n",
    "    # try to find the data file local. If it doesn't exists download it.\n",
    "    cache_path = os.path.join(\"data\", \"iot\")\n",
    "    cache_file = os.path.join(cache_path, \"solar.csv\")\n",
    "    if not os.path.exists(cache_path):\n",
    "        os.makedirs(cache_path)\n",
    "    if not os.path.exists(cache_file):\n",
    "        urlretrieve(input_url, cache_file)\n",
    "        print(\"downloaded data successfully from \", input_url)\n",
    "    else:\n",
    "        print(\"using cache for \", input_url)\n",
    "    \n",
    "    df = pd.read_csv(cache_file, index_col=\"time\", parse_dates=['time'], dtype=np.float32)\n",
    "    \n",
    "    df[\"date\"] = df.index.date\n",
    "    \n",
    "    # normalize data\n",
    "    df['solar.current'] /= normalize\n",
    "    df['solar.total'] /= normalize\n",
    "    \n",
    "    # group by day, find the max for a day and add a new column .max\n",
    "    grouped = df.groupby(df.index.date).max()\n",
    "    grouped.columns = [\"solar.current.max\", \"solar.total.max\", \"date\"]\n",
    "\n",
    "    # merge continuous readings and daily max values into a single frame\n",
    "    df_merged = pd.merge(df, grouped, right_index=True, on=\"date\")\n",
    "    df_merged = df_merged[[\"solar.current\", \"solar.total\",\n",
    "                           \"solar.current.max\", \"solar.total.max\"]]\n",
    "    # we group by day so we can process a day at a time.\n",
    "    grouped = df_merged.groupby(df_merged.index.date)\n",
    "    per_day = []\n",
    "    for _, group in grouped:\n",
    "        per_day.append(group)\n",
    "\n",
    "    # split the dataset into train, validatation and test sets on day boundaries\n",
    "    val_size = int(len(per_day) * val_size)\n",
    "    test_size = int(len(per_day) * test_size)\n",
    "    next_val = 0\n",
    "    next_test = 0\n",
    "\n",
    "    result_x = {\"train\": [], \"val\": [], \"test\": []}\n",
    "    result_y = {\"train\": [], \"val\": [], \"test\": []}    \n",
    "\n",
    "    # generate sequences a day at a time\n",
    "    for i, day in enumerate(per_day):\n",
    "        # if we have less than 8 datapoints for a day we skip over the\n",
    "        # day assuming something is missing in the raw data\n",
    "        total = day[\"solar.total\"].values\n",
    "        if len(total) < 8:\n",
    "            continue\n",
    "        if i >= next_val:\n",
    "            current_set = \"val\"\n",
    "            next_val = i + int(len(per_day) / val_size)\n",
    "        elif i >= next_test:\n",
    "            current_set = \"test\"\n",
    "            next_test = i + int(len(per_day) / test_size)\n",
    "        else:\n",
    "            current_set = \"train\"\n",
    "        max_total_for_day = np.array(day[\"solar.total.max\"].values[0])\n",
    "        for j in range(2, len(total)):\n",
    "            result_x[current_set].append(total[0:j])\n",
    "            result_y[current_set].append([max_total_for_day])\n",
    "            if j >= time_steps:\n",
    "                break\n",
    "    # make result_y a numpy array\n",
    "    for ds in [\"train\", \"val\", \"test\"]:\n",
    "        result_y[ds] = np.array(result_y[ds])\n",
    "    return result_x, result_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data caching\n",
    "For routine testing we would like to cache the data locally when available. If it is not available from the cache locations we shall download."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using cache for  https://www.cntk.ai/jup/dat/solar.csv\n"
     ]
    }
   ],
   "source": [
    "# We keep upto 14 inputs from a day\n",
    "TIMESTEPS = 14\n",
    "\n",
    "# 20000 is the maximum total output in our dataset. We normalize all values with \n",
    "# this so our inputs are between 0.0 and 1.0 range.\n",
    "NORMALIZE = 20000\n",
    "\n",
    "X, Y = generate_solar_data(\"https://www.cntk.ai/jup/dat/solar.csv\", \n",
    "                           TIMESTEPS, normalize=NORMALIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Utility for data fetching**\n",
    "\n",
    "`next_batch()` yields the next batch for training. We use variable size sequences supported by CNTK and batches are a list of numpy arrays where the numpy arrays have variable length. \n",
    "\n",
    "A standard practice is to shuffle batches with each epoch. We don't do this here because we want to be able to graph the data that is easily interpretable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# process batches of 10 days\n",
    "BATCH_SIZE = TIMESTEPS * 10\n",
    "\n",
    "def next_batch(x, y, ds):\n",
    "    \"\"\"get the next batch for training\"\"\"\n",
    "\n",
    "    def as_batch(data, start, count):\n",
    "        return data[start:start + count]\n",
    "\n",
    "    for i in range(0, len(x[ds]), BATCH_SIZE):\n",
    "        yield as_batch(X[ds], i, BATCH_SIZE), as_batch(Y[ds], i, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Understand the data format**\n",
    "\n",
    "You can now see the sequence we are going to feed to the LSTM. Note if we have less than 8 datapoints for a day we skip over the day assuming something is missing in the raw data. If we get more than 14 data points in a day we truncate the readings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.       ,  0.0006985], dtype=float32),\n",
       " array([ 0.       ,  0.0006985,  0.0033175], dtype=float32),\n",
       " array([ 0.       ,  0.0006985,  0.0033175,  0.010375 ], dtype=float32)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['train'][0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.23899999],\n",
       "       [ 0.23899999],\n",
       "       [ 0.23899999]], dtype=float32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y['train'][0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Creation (LSTM network)\n",
    "\n",
    "Corresponding  to the maximum possible 14 data points in the input sequence, we model our network with 14 LSTM cells, 1 cell for each data point we take during the day. Since the input sequences can be between 8 and 14 data points per sequence, we take the advantage of CNTK support for variable sequences as input to a LSTM so we can feed our sequences as-is with no additional need for padding.\n",
    "\n",
    "The output of the neural network is the total output for the day and each sequence for a given day has the same total output.\n",
    "\n",
    "For example:\n",
    "```\n",
    "1.7,11.4 -> 10300\n",
    "1.7,11.4,67.5 -> 10300\n",
    "1.7,11.4,67.5,250.5 ... -> 10300\n",
    "1.7,11.4,67.5,250.5,573.5 -> 10300\n",
    "```\n",
    "\n",
    "The outputs from the LSTMs are fed into a dense layer and we randomly dropout 20% of the values to not overfit the model to the training set. The output of the dense layer becomes the prediction our model generates.\n",
    "\n",
    "Notice: We lost the timestamp altogether; in our model only the sequences of readings matter. \n",
    "\n",
    "Our LSTM model has the following design:\n",
    "![lstm](https://guschmueds.blob.core.windows.net/datasets/2.png)\n",
    "\n",
    "The network model is an exact translation of the network diagram above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Specify the internal-state dimensions of the LSTM cell\n",
    "H_DIMS = 15\n",
    "def create_model(x):\n",
    "    \"\"\"Create the model for time series prediction\"\"\"\n",
    "    with C.layers.default_options(initial_state = 0.1):\n",
    "        m = C.layers.Recurrence(C.layers.LSTM(H_DIMS))(x)\n",
    "        m = C.sequence.last(m)\n",
    "        m = C.layers.Dropout(0.2)(m)\n",
    "        m = C.layers.Dense(1)(m)\n",
    "        return m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "Before we can start training we need to bind our input variables for the model and define what optimizer we want to use. For this example we choose the `adam` optimizer. We choose `squared_error` as our loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# input sequences\n",
    "x = C.sequence.input_variable(1)\n",
    "\n",
    "# create the model\n",
    "z = create_model(x)\n",
    "\n",
    "# expected output (label), also the dynamic axes of the model output\n",
    "# is specified as the model of the label input\n",
    "l = C.input_variable(1, dynamic_axes=z.dynamic_axes, name=\"y\")\n",
    "\n",
    "# the learning rate\n",
    "learning_rate = 0.005\n",
    "lr_schedule = C.learning_parameter_schedule(learning_rate)\n",
    "\n",
    "# loss function\n",
    "loss = C.squared_error(z, l)\n",
    "\n",
    "# use squared error to determine error for now\n",
    "error = C.squared_error(z, l)\n",
    "\n",
    "# use adam optimizer\n",
    "momentum_schedule = C.momentum_schedule(0.9, minibatch_size=BATCH_SIZE)\n",
    "learner = C.fsadagrad(z.parameters, \n",
    "                      lr = lr_schedule, \n",
    "                      momentum = momentum_schedule)\n",
    "trainer = C.Trainer(z, (loss, error), [learner])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to start training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0, loss: 0.1058\n",
      "epoch: 10, loss: 0.0244\n",
      "epoch: 20, loss: 0.0138\n",
      "epoch: 30, loss: 0.0079\n",
      "epoch: 40, loss: 0.0071\n",
      "epoch: 50, loss: 0.0071\n",
      "epoch: 60, loss: 0.0081\n",
      "epoch: 70, loss: 0.0068\n",
      "epoch: 80, loss: 0.0068\n",
      "epoch: 90, loss: 0.0073\n",
      "Training took 258.7 sec\n"
     ]
    }
   ],
   "source": [
    "# training\n",
    "loss_summary = []\n",
    "\n",
    "start = time.time()\n",
    "for epoch in range(0, EPOCHS):\n",
    "    for x_batch, l_batch in next_batch(X, Y, \"train\"):\n",
    "        trainer.train_minibatch({x: x_batch, l: l_batch})\n",
    "        \n",
    "    if epoch % (EPOCHS / 10) == 0:\n",
    "        training_loss = trainer.previous_minibatch_loss_average\n",
    "        loss_summary.append(training_loss)\n",
    "        print(\"epoch: {}, loss: {:.4f}\".format(epoch, training_loss))\n",
    "\n",
    "print(\"Training took {:.1f} sec\".format(time.time() - start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A look how the loss function shows how the model is converging:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRCREFPoiIiGi0BcRCZH/DzZP+boUYdVqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27d9d1efcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(loss_summary, label='training loss');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us validate the training validation and test dataset. We use mean squared error as measure which might be a little simplistic. A method that would define a ratio how many predictions have been inside a given tolerance would make a better measure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# validate\n",
    "def get_mse(X,Y,labeltxt):\n",
    "    result = 0.0\n",
    "    for x1, y1 in next_batch(X, Y, labeltxt):\n",
    "        eval_error = trainer.test_minibatch({x : x1, l : y1})\n",
    "        result += eval_error\n",
    "    return result/len(X[labeltxt])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse for train: 0.000082\n",
      "mse for val: 0.000080\n"
     ]
    }
   ],
   "source": [
    "# Print the train and validation errors\n",
    "for labeltxt in [\"train\", \"val\"]:\n",
    "    print(\"mse for {}: {:.6f}\".format(labeltxt, get_mse(X, Y, labeltxt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse for test: 0.000080\n"
     ]
    }
   ],
   "source": [
    "# Print the test error\n",
    "labeltxt = \"test\"\n",
    "print(\"mse for {}: {:.6f}\".format(labeltxt, get_mse(X, Y, labeltxt)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize results\n",
    "\n",
    "Our model has been trained well, given the train, validation and test errors are in the same ball park. Predicted time series data renders well with visualization of the results. Let us take our newly created model, make predictions and plot them against the actual readings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAHVCAYAAAA6rtfLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27d9d1b8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# predict\n",
    "f, a = plt.subplots(2, 1, figsize=(12, 8))\n",
    "for j, ds in enumerate([\"val\", \"test\"]):\n",
    "    results = []\n",
    "    for x_batch, _ in next_batch(X, Y, ds):\n",
    "        pred = z.eval({x: x_batch})\n",
    "        results.extend(pred[:, 0])\n",
    "    # because we normalized the input data we need to multiply the prediction\n",
    "    # with SCALER to get the real values.\n",
    "    a[j].plot((Y[ds] * NORMALIZE).flatten(), label=ds + ' raw');\n",
    "    a[j].plot(np.array(results) * NORMALIZE, label=ds + ' pred');\n",
    "    a[j].legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we let the model train for 2000 epochs the predictions are close to the actual data and follow the right pattern."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Suggested activity**\n",
    "\n",
    "So what we do with this model? A practical application would be to generate alerts if the actual output is not in line with the prediction, for example if one of the panels is failing. The solar array that goes with our dataset has 16 panels. If we'd want to detect failure without generating false alerts, the accuracy of our prediction would need to be at least 1 - 1/16, around 94%. Our model is close to this but would most likely generate occasional false alerts.\n",
    "\n",
    "- Improve the model by training for more epochs.\n",
    "- Further preprocess the training set to smooth-out missing values\n",
    "- Try out more complex networks.\n",
    "\n",
    "However, in our experience with time series data one can achieve significant accuracy improvement comes from higher resolution training data, for example reading a data point every 5 minutes instead of every 30 minutes.\n",
    "\n",
    "We hope this tutorial gets you started on time series prediction with neural networks."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
